An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

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Rubul Kumar Bania 1,*

1. Department of Computer Applications, North-Eastern Hill University, Tura Campus,Meghalaya,India

* Corresponding author.


Received: 8 Aug. 2017 / Revised: 14 Sep. 2017 / Accepted: 16 Oct. 2017 / Published: 8 Jan. 2018

Index Terms

Feature selection, lower approximation, fuzzy set, rough set


Selection of useful information from a large data collection is an important and challenging problem. Feature selection refers to the problem of selecting relevant features from a given dataset which produces the most predictive outcome as the original features maintain before the selection. Rough set theory (RST) and its extension are the most successful mathematical tools for feature selection from a given dataset. This paper starts with an outline of the fundamental concepts behind the rough set and fuzzy rough set based feature grouping techniques which are related to supervise feature selection. Supervised Quickreduct (QR) and fuzzy-rough feature grouping Quickreduct (FQR) algorithms are highlighted here. Then an enhanced version of FQR method is proposed here which is based on rough set dependency criteria with feature significance measure that select a minimal subset of features. Also, the termination condition of the base method is modified. Experimental studies of the algorithms are carried out on five public domain benchmark datasets available in UCI machine learning repository. JRip and J48 classifier are used to measure the classification accuracy. The performance of the proposed method is found to be satisfactory in comparison with other methods.

Cite This Paper

Rubul Kumar Bania,"An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.4, No.1, pp.71-82, 2018. DOI: 10.5815/ijmsc.2018.01.05


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